Urban planning is a multi-objective problem. The traditional urban planning optimization decision aims to solve a single-objective optimization problem with one or more bounded-constraints, such as traffic, environment, etc. To satisfy the individual requirement for decision-makers in urban planning, in this paper, we analyze the limitation of the traditional methods and propose a new approach that supports multi-objective optimization. The approach considers the decision essentials as optimization objectives rather than decision constraints, and overcomes the limitations of the traditional methods. Furthermore, a new decision method using multi-objective evolutionary algorithm based on Pareto concepts is given. The initial experimental results have shown that our multi-objective planning approach can effectively produce more than one Pareto optimization solution compromising all decision essentials within one single running, and decision-makers can choose the solution they need among the alternatives.
